RESUMO
The planning of hospital beds is among the most debated problems in healthcare. Despite being an important issue, many initiatives have failed to sustain services improvements, resulting in high costs and also high refusal rates. The stochastic problem involves conflicting criteria, therefore, we propose a Simulation-Optimisation approach to solve it. The Evolutionary Algorithm NSGA-II drives the process, and the solutions are validated and evaluated via Discrete Event Simulation. An application is performed in one of the health regions of the state of Minas Gerais, Brazil, where the public health system assists nearly 80% of the patients. The results pointed out that the proposed approach could find efficient and feasible solutions for the problem. Therefore, it is a good alternative to empirical methods currently used in Brazil to set hospital beds allocation.
Assuntos
Algoritmos , Hospitais , Brasil , Simulação por Computador , HumanosRESUMO
This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied to a general setting of the machine learning problem. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. In this paper, one general Q-norm method to compute the machine complexity is presented, and, as a particular practical case, the minimum gradient method (MGM) is derived relying on the definition of the fat-shattering dimension. A practical mechanism for parallel layer perceptron (PLP) network training, involving only quasi-convex functions, is generated using the aforementioned definitions. Experimental results on 15 different benchmarks are presented, which show the potential of the proposed ideas.